The principal aim of this project is to test the hypothesis of general multi-specificity for the combining regions of antibodies and other kinds of receptors. Receptor sites, according to theory developed earlier in this project, should be capable of interacting with virtually any substance in a manner that will lower the standard free energy of the system and thus exhibit an equilibrium association constant greater than 1. Most associations will be weaker than ones commonly measured, but occasional substances may bind to a receptor with affinities high enough to affect biological function. Their structures may not necessarily resemble those of the recognized effector, substrate or antigen. The above hypothesis is being tested in the following way: Radiolabeled, monoclonal antibodies or solubilized receptors are passed through small, affinity chromatography columns. Accurate measurements are made of the retention (retardation) caused by a matrix-bound reference ligand in the presence and absence of many, diverse, suitably large compounds. The resulting retention values are employed directly in calculating association constants for these compounds and the receptor site. The distribution of constants provides a description of the receptor's multispecific character. The technique of quantitative affinity chromatography, developed in this study, provides a general and effective means for estimating very low to moderately strong association constants for antibodies and requires very small samples. Large affinity probes that can be covalently bound to affinity matrices have been synthesized by systematic strategies developed in an earlier phase of this project. Multispecific associations with such probes will be usefully employed in extending the scope of specific, affinity-based separations. Knowledge of multispecificity frequencies should play an essential role in understanding specificity (selectivity) in biological recognition and control. Special attention will be given to applying these findings to models of immune systems and control networks.